We propose an approach to measuring the state of the economy via textual analysis of business news. From the full text content of 800,000 Wall Street Journal articles for 1984–2017, we estimate a topic model that summarizes business news as easily interpretable topical themes and quantifies the proportion of news attention allocated to each theme at each point in time. We then use our news attention estimates as inputs into statistical models of numerical economic time series. We demonstrate that these text-based inputs accurately track a wide range of economic activity measures and that they have incremental forecasting power for macroeconomic outcomes, above and beyond standard numerical predictors. Finally, we use our model to retrieve the news-based narratives that underly “shocks” in numerical economic data.
Yale School of Management
Yale SOM; AQR Capital Management, LLC; National Bureau of Economic Research (NBER)
Washington University in St. Louis - John M. Olin Business School
University of Chicago - Booth School of Business
Reference: Bybee, Leland and Kelly, Bryan T. and Manela, Asaf and Xiu, Dacheng, Business News and Business Cycles, forthcoming in the Journal of Finance.